{"title":"Revolutionizing Healthcare: The Role of Machine Learning in the Health Sector","authors":"Mithun Sarker","doi":"10.60087/jaigs.v2i1.p47","DOIUrl":null,"url":null,"abstract":"Traditional healthcare systems have grappled with meeting the diverse needs of millions of patients, resulting in inefficiencies and suboptimal outcomes. However, the emergence of machine learning (ML) has ushered in a transformative paradigm shift towards value-based treatment, empowering healthcare providers to deliver personalized and highly effective care. Modern healthcare equipment and devices now integrate internal applications that collect and store comprehensive patient data, providing a rich resource for ML-driven predictive models. In this research article, we explore the profound impact of ML on contemporary healthcare, emphasizing its potential to significantly enhance patient care and optimize resource allocation. Our study presents a robust predictive model capable of accurately forecasting patient diseases based on input information and various parameters, leveraging extensive datasets encompassing diverse patient populations. We compared several ML algorithms, including Logistic Regression (accuracy: 0.796875), K-Nearest Neighbors (accuracy: 0.7864583333333334), XG Boost (accuracy: 0.78125), and PyTorch (accuracy: 0.7337662337662337), to identify the best-performing model. The achieved accuracies underscore the effectiveness of these ML techniques in disease prediction and underscore the potential for improving patient outcomes. Beyond the technical aspects, we explore the broader implications of value-based treatment and the integration of ML for various healthcare stakeholders. By emphasizing the numerous benefits of personalized and proactive medical care, our findings illustrate the substantial potential of ML-driven predictive healthcare models to revolutionize traditional healthcare systems. The adoption of ML in healthcare lays the foundation for a more efficient, effective, and patient-centered medical ecosystem, supporting the sustainability and adaptability of healthcare systems in the face of expanding patient populations and complex medical needs. This article significantly contributes to the field by providing comprehensive insights into the experimental stages, showcasing the achieved results, and highlighting the key conclusions derived from our study. By addressing the limitations of the previous abstract, we ensure a more informative and substantial overview of our research, offering valuable knowledge for researchers, practitioners, and decision-makers striving to leverage the power of ML in healthcare innovation.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"20 12","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.60087/jaigs.v2i1.p47","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Traditional healthcare systems have grappled with meeting the diverse needs of millions of patients, resulting in inefficiencies and suboptimal outcomes. However, the emergence of machine learning (ML) has ushered in a transformative paradigm shift towards value-based treatment, empowering healthcare providers to deliver personalized and highly effective care. Modern healthcare equipment and devices now integrate internal applications that collect and store comprehensive patient data, providing a rich resource for ML-driven predictive models. In this research article, we explore the profound impact of ML on contemporary healthcare, emphasizing its potential to significantly enhance patient care and optimize resource allocation. Our study presents a robust predictive model capable of accurately forecasting patient diseases based on input information and various parameters, leveraging extensive datasets encompassing diverse patient populations. We compared several ML algorithms, including Logistic Regression (accuracy: 0.796875), K-Nearest Neighbors (accuracy: 0.7864583333333334), XG Boost (accuracy: 0.78125), and PyTorch (accuracy: 0.7337662337662337), to identify the best-performing model. The achieved accuracies underscore the effectiveness of these ML techniques in disease prediction and underscore the potential for improving patient outcomes. Beyond the technical aspects, we explore the broader implications of value-based treatment and the integration of ML for various healthcare stakeholders. By emphasizing the numerous benefits of personalized and proactive medical care, our findings illustrate the substantial potential of ML-driven predictive healthcare models to revolutionize traditional healthcare systems. The adoption of ML in healthcare lays the foundation for a more efficient, effective, and patient-centered medical ecosystem, supporting the sustainability and adaptability of healthcare systems in the face of expanding patient populations and complex medical needs. This article significantly contributes to the field by providing comprehensive insights into the experimental stages, showcasing the achieved results, and highlighting the key conclusions derived from our study. By addressing the limitations of the previous abstract, we ensure a more informative and substantial overview of our research, offering valuable knowledge for researchers, practitioners, and decision-makers striving to leverage the power of ML in healthcare innovation.
传统的医疗保健系统一直在努力满足数百万患者的不同需求,导致效率低下、疗效不佳。然而,机器学习(ML)的出现带来了向基于价值的治疗模式的转变,使医疗服务提供者有能力提供个性化和高效的医疗服务。现代医疗保健设备和装置现在集成了内部应用程序,可收集和存储全面的患者数据,为 ML 驱动的预测模型提供了丰富的资源。在这篇研究文章中,我们探讨了 ML 对当代医疗保健的深远影响,强调了它在显著增强患者护理和优化资源分配方面的潜力。我们的研究基于输入信息和各种参数,利用涵盖不同患者群体的广泛数据集,提出了一种能够准确预测患者疾病的强大预测模型。我们比较了几种 ML 算法,包括逻辑回归(准确率:0.796875)、K-近邻(准确率:0.78645833333334)、XG Boost(准确率:0.78125)和 PyTorch(准确率:0.7337662337662337),以确定表现最佳的模型。所取得的准确率突出表明了这些 ML 技术在疾病预测方面的有效性,并彰显了改善患者预后的潜力。除了技术方面,我们还探讨了基于价值的治疗和整合 ML 对不同医疗保健利益相关者的更广泛影响。通过强调个性化和前瞻性医疗保健的诸多益处,我们的研究结果表明了人工智能驱动的预测性医疗保健模型在彻底改变传统医疗保健系统方面的巨大潜力。在医疗保健领域采用人工智能为建立一个更加高效、有效和以患者为中心的医疗生态系统奠定了基础,从而支持医疗保健系统在面对不断扩大的患者群体和复杂的医疗需求时的可持续性和适应性。本文全面介绍了实验阶段的情况,展示了取得的成果,并强调了从我们的研究中得出的重要结论,从而为该领域做出了重大贡献。通过解决上一篇摘要的局限性,我们确保对我们的研究进行更翔实、更实质性的概述,为努力在医疗创新中利用 ML 的力量的研究人员、从业人员和决策者提供有价值的知识。